ArticleAbdominal radiology (New York)2025
Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis.
Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Integrated multi-task learning framework for hepatocellular carcinoma segmentation and histological grading using fused multi-phase MRI.Abdominal radiology (New York) · 2026Article
- Uncertainty-Aware Framework for CT Radiation Dose Optimization in the Active Surveillance of Small Renal Masses: Clinical and Radiological Considerations.Diagnostics (Basel, Switzerland) · 2026Article
- Association between visceral adipose tissue measured by deep neural network architecture and chronic kidney disease.Scientific reports · 2026Article
- Review
- Fully Automatic Artificial Intelligence Liver Anatomy Segmentation in the Management of Colorectal Liver Metastases.Cureus · 2025Article
- Machine learning based on automated 3D radiomics features to classify prostate cancer in patients with prostate-specific antigen levels of 4-10 ng/mL.Translational andrology and urology · 2025Article
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Abstract
purposeTo develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs.
methodsFully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC).
resultsThe models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement.
conclusionOur nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.
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